Quantification of Lung Abnormalities in Cystic Fibrosis using Deep Networks

Cystic fibrosis is a genetic disease which may appear in early life with structural abnormalities in lung tissues. We propose to detect these abnormalities using a texture classification approach. Our method is a cascade of two convolutional neural networks. The first network detects the presence of abnormal tissues. The second network identifies the type of the structural abnormalities: bronchiectasis, atelectasis or mucus plugging.We also propose a network computing pixel-wise heatmaps of abnormality presence learning only from the patch-wise annotations. Our database consists of CT scans of 194 subjects. We use 154 subjects to train our algorithms and the 40 remaining ones as a test set. We compare our method with random forest and a single neural network approach. The first network reaches an accuracy of 0,94 for disease detection, 0,18 higher than the random forest classifier and 0,37 higher than the single neural network. Our cascade approach yields a final class-averaged F1-score of 0,33, outperforming the baseline method and the single network by 0,10 and 0,12.Comment: SPIE - Medical Imaging 2018: Image Processin

Spectral Data Augmentation Techniques to quantify Lung Pathology from CT-images

Data augmentation is of paramount importance in biomedical image processing tasks, characterized by inadequate amounts of labelled data, to best use all of the data that is present. In-use techniques range from intensity transformations and elastic deformations, to linearly combining existing data points to make new ones. In this work, we propose the use of spectral techniques for data augmentation, using the discrete cosine and wavelet transforms. We empirically evaluate our approaches on a CT texture analysis task to detect abnormal lung-tissue in patients with cystic fibrosis. Empirical experiments show that the proposed spectral methods perform favourably as compared to the existing methods. When used in combination with existing methods, our proposed approach can increase the relative minor class segmentation performance by 44.1% over a simple replication baseline.Comment: 5 pages including references, accepted as Oral presentation at IEEE ISBI 202

Imaging and Treatment of Bronchiectasis:Chest computed tomography to diagnose bronchiectasis and to optimise inhalation treatment

This thesis covers image analysis of bronchiectasis and treatment with inhalation antibiotics